Meng Han

1.6k total citations · 1 hit paper
50 papers, 1.1k citations indexed

About

Meng Han is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics. According to data from OpenAlex, Meng Han has authored 50 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 23 papers in Information Systems and 15 papers in Computational Theory and Mathematics. Recurrent topics in Meng Han's work include Data Mining Algorithms and Applications (21 papers), Imbalanced Data Classification Techniques (15 papers) and Rough Sets and Fuzzy Logic (14 papers). Meng Han is often cited by papers focused on Data Mining Algorithms and Applications (21 papers), Imbalanced Data Classification Techniques (15 papers) and Rough Sets and Fuzzy Logic (14 papers). Meng Han collaborates with scholars based in China, United States and Brazil. Meng Han's co-authors include Junming Shao, Zhitong Zhao, Zhao Kang, Zenglin Xu, Wang-Tao Zhou, Xiaojuan Li, Le Wang, Ni Zhang, Xi Ma and Michael A. Brown and has published in prestigious journals such as PLoS ONE, Analytical Biochemistry and Scientific Reports.

In The Last Decade

Meng Han

42 papers receiving 1.1k citations

Hit Papers

Large-Scale Multi-View Subspace Clustering in Linear Time 2020 2026 2022 2024 2020 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Large-Scale Multi-View Subspace Clustering in Linear Time
    2020 357 citations Meng Han et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Meng Han China 13 438 336 163 124 95 50 1.1k
Mehdi Agha Sarram Iran 12 413 0.9× 286 0.9× 136 0.8× 54 0.4× 98 1.0× 38 898
Yanli Ren China 21 471 1.1× 577 1.7× 189 1.2× 218 1.8× 57 0.6× 120 1.5k
Mohammad Ali Zare Chahooki Iran 10 281 0.6× 281 0.8× 89 0.5× 51 0.4× 90 0.9× 34 627
Derya Avcı Türkiye 19 500 1.1× 305 0.9× 60 0.4× 77 0.6× 25 0.3× 47 1.3k
Jingbin Wang China 15 394 0.9× 736 2.2× 110 0.7× 59 0.5× 13 0.1× 50 1.4k
Fan Zhao China 17 233 0.5× 479 1.4× 118 0.7× 65 0.5× 170 1.8× 74 1.2k
Jinlong Huang China 17 875 2.0× 384 1.1× 35 0.2× 64 0.5× 45 0.5× 39 1.1k
Dongdong Cheng China 18 694 1.6× 326 1.0× 31 0.2× 56 0.5× 35 0.4× 42 884
Jyotismita Chaki India 18 359 0.8× 280 0.8× 48 0.3× 61 0.5× 17 0.2× 49 1.3k

Countries citing papers authored by Meng Han

Since Specialization
Citations

This map shows the geographic impact of Meng Han's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Meng Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Meng Han more than expected).

Fields of papers citing papers by Meng Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Meng Han. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Meng Han. The network helps show where Meng Han may publish in the future.

Co-authorship network of co-authors of Meng Han

This figure shows the co-authorship network connecting the top 25 collaborators of Meng Han. A scholar is included among the top collaborators of Meng Han based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Meng Han. Meng Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Guo, Tingting, Hongyi Gao, Meng Han, et al.. (2025). Mixed-linker Zr-based metal-organic framework as a highly sensitive probe for ultrafast and selective detection of fluoride ions. Environmental Research. 276. 121469–121469. 1 indexed citations
2.
Zhang, Ruihua, et al.. (2025). Damped sliding based mining of average utility-driven sequential patterns over uncertain data streams. Neurocomputing. 647. 130669–130669.
3.
Jia, Yuanyuan, et al.. (2025). Dysbiosis of gut and urinary microbiota in urolithiasis patients and post-surgical cases. Frontiers in Cellular and Infection Microbiology. 15. 1633783–1633783.
4.
Han, Meng, et al.. (2025). A review of meta-heuristic high utility patterns mining methods. Knowledge and Information Systems. 67(7). 5469–5510.
5.
Liu, Yan, et al.. (2024). Associations between gut microbiota and osteoporosis or osteopenia in a cohort of Chinese Han youth. Scientific Reports. 14(1). 20948–20948. 8 indexed citations
6.
7.
Han, Meng, et al.. (2023). Hybrid Sampling and Dynamic Weighting-Based Classification Method for Multi-Class Imbalanced Data Stream. Applied Sciences. 13(10). 5924–5924. 5 indexed citations
8.
Han, Meng, et al.. (2023). High utility pattern mining algorithm over data streams using ext-list.. Applied Intelligence. 53(22). 27072–27095. 3 indexed citations
9.
Han, Meng, et al.. (2023). FCHM-stream: fast closed high utility itemsets mining over data streams. Knowledge and Information Systems. 65(6). 2509–2539. 5 indexed citations
10.
Han, Meng, et al.. (2022). A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream. ACM Transactions on Knowledge Discovery from Data. 17(5). 1–21. 8 indexed citations
11.
Han, Meng, et al.. (2022). An overview of high utility itemsets mining methods based on intelligent optimization algorithms. Knowledge and Information Systems. 64(11). 2945–2984. 11 indexed citations
12.
Wang, Le, et al.. (2021). Review of Classification Methods on Unbalanced Data Sets. IEEE Access. 9. 64606–64628. 181 indexed citations
13.
Han, Meng, et al.. (2021). Mining high utility pattern with negative items in dynamic databases. International Journal of Intelligent Systems. 37(8). 5325–5353. 5 indexed citations
14.
15.
Mahmud, Saifuddin, et al.. (2019). Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting. Analytical Biochemistry. 589. 113507–113507. 60 indexed citations
16.
Lu, Yunmei, et al.. (2018). Privacy preserving feature selection and Multiclass Classification for horizontally distributed data. Mathematical Foundations of Computing. 1(4). 331–348. 7 indexed citations
17.
Han, Meng, Jian Ding, & Juan Li. (2017). TDMCS: an efficient method for mining closed frequent patterns over data streams based on time decay model.. The International Arab Journal of Information Technology. 14. 851–860. 7 indexed citations
18.
Han, Meng, Congmin Wang, Ping Liu, et al.. (2017). Dietary Fiber Gap and Host Gut Microbiota. Protein and Peptide Letters. 24(5). 388–396. 51 indexed citations
19.
Han, Meng, Zhihai Wang, & Yashu Liu. (2014). Selecting One Dependency Estimators in Bayesian Network Using Different MDL Scores and Overfitting Criterion. Journal of information science and engineering. 30(2). 371–385. 6 indexed citations
20.
Han, Meng. (2008). Design of Examination System Model based on Networks. Microcomputer Information.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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